How to resolve "IndexError: too many indices for array"

Question

My code below is giving me the following error "IndexError: too many indices for array". I am quite new to machine learning so I do not have any idea about how to solve this. Any kind of help would be appreciated.

train = pandas.read_csv("D:/...input/train.csv")


xTrain = train.iloc[:,0:54]
yTrain = train.iloc[:,54:]


from sklearn.cross_validation import cross_val_score
clf = LogisticRegression(multi_class='multinomial')
scores = cross_val_score(clf, xTrain, yTrain, cv=10, scoring='accuracy')
print('****Results****')
print(scores.mean())

Answer 1

Step by Step Explanation of ML (Machine Learning) Code with Pandas Dataframe :

1. Seperating Predictor and Target Columns into X and y Respectively.

2. Splitting Training data (X_train,y_train) and Testing Data (X_test,y_test).

3. Calculating Cross-Validated AUC (Area Under the Curve). Got an Error “IndexError: too many indices for array” due to y_train since it was expecting a 1-D Array but Fetched 2-D Array which is a Mismatch. After Replacing the code 'y_train' with y_train['y'] code worked like a Charm.

---

   # Importing Packages :

   import pandas as pd

   from sklearn.model_selection import cross_val_score

   from sklearn.model_selection import StratifiedShuffleSplit

   # Seperating Predictor and Target Columns into X and y Respectively :
   # df -> Dataframe extracted from CSV File

   data_X = df.drop(['y'], axis=1) 
   data_y = pd.DataFrame(df['y'])

   # Making a Stratified Shuffle Split of Train and Test Data (test_size=0.3 Denotes 30 % Test Data and Remaining 70% Train Data) :

   rs = StratifiedShuffleSplit(n_splits=2, test_size=0.3,random_state=2)       
   rs.get_n_splits(data_X,data_y)

   for train_index, test_index in rs.split(data_X,data_y):

       # Splitting Training and Testing Data based on Index Values :

       X_train,X_test = data_X.iloc[train_index], data_X.iloc[test_index]
       y_train,y_test = data_y.iloc[train_index], data_y.iloc[test_index]

       # Calculating 5-Fold Cross-Validated AUC (cv=5) - Error occurs due to Dimension of **y_train** in this Line :

       classify_cross_val_score = cross_val_score(classify, X_train, y_train, cv=5, scoring='roc_auc').mean()

       print("Classify_Cross_Val_Score ",classify_cross_val_score) # Error at Previous Line.

       # Worked after Replacing 'y_train' with y_train['y'] in above Line 
       # where y is the ONLY Column (or) Series Present in the Pandas Data frame 
       # (i.e) Target variable for Prediction :

       classify_cross_val_score = cross_val_score(classify, X_train, y_train['y'], cv=5, scoring='roc_auc').mean()

       print("Classify_Cross_Val_Score ",classify_cross_val_score)

       print(y_train.shape)

       print(y_train['y'].shape)

Output :

    Classify_Cross_Val_Score  0.7021433588790991
    (31647, 1) # 2-D
    (31647,)   # 1-D

Note : from sklearn.model_selection import cross_val_score. cross_val_score has been imported from sklearn.model_selection and NOT from sklearn.cross_validation which is Deprecated.